Distributed estimation for nonlinear systems with correlated multiplicative noises and randomly delayed measurements

This paper presents the distributed state estimation for nonlinear systems with randomly delayed measurements under correlated additive and multiplicative noises (NSAMD). In the considered problem, the interested state is observed by multiple sensor clusters, and the corresponding measurement data is sent to the remote distributed processing network via data transmission, along with the random delay obeying the first-order Markov chain. Then, the distributed Gaussian-information filter (DGIF) is presented to pursue a tradeoff between estimate accuracy and computation time, including a novel Gaussian filter for NSAMD with the estimated delay probability online (abbreviated as GAMDF) in the sense of minimizing the estimate error covariance in the single local processing node/unit, and a distributed information filter form to give an efficient distributed fusion via consensus strategy based on the statistical linear regression applied to nonlinear measurement equations. A numerical example is simulated to validate the proposed method in a single processing unit and the distributed processing network.